A weak KAM approach to the periodic stationary Hartree equation

AbstractWe present, through weak KAM theory, an investigation of the stationary Hartree equation in the periodic setting. More in details, we study the Mean Field asymptotics of quantum many body operators thanks to various integral identities providing the energy of the ground state and the minimum value of the Hartree functional. Finally, the ground state of the multiple-well case is studied in the semiclassical asymptotics thanks to the Agmon metric

Employee attitudes and (Digital) collaboration data: a preliminary analysis in the HRM field

The digital transformation of organizations is making workplace collaboration more and more powerful and work always "observable"; however, the informational and managerial potential of the generated data is still largely unutilized in Human Resource Management (HRM). Our research, conducted in collaboration with business engineers and economists, aims at exploring the relationship between digital work behaviors and employee attitudes. This paper is a work-in-progress contribution that presents a preliminary phase of data analysis we performed on a collection of Enterprise Collaboration Software (ECS) data. In the exploratory data analysis step, we analyze data in their original table format and elaborate it according to the user who performed the action and the performed action. Then, we move to a graph representation in order to make explicit the interaction between users and the objects of their actions. Finally, we introduce the concept of employee-attitude-oriented pattern as a mean to derive significant views over the overall graph and discuss Social Network Analysis (SNA) approaches that can be exploited for our purposes

rCASC implementation in Laniakea: porting containerization-based-reproducibility to a cloud Galaxy on-demand platform

Integrating rCASC in Laniakea: rCASC, Cluster Analysis of Single Cells [Alessandri et al. BioRxiv], is part of the reproducible-bioinformatics.org project and provides single cell analysis functionalities within the reproducible rules described by Sandve et al. [PLoS Comp Biol. 2013]. Laniakea [Tangaro et al. BioRxiv Bioinformatics] provides the possibility to automate the creation of Galaxy-based virtualized environments through an easy setup procedure, providing an on-demand workspace ready to be used by life scientists and bioinformaticians. The final goal is to offer rCASC as a Galaxy flavor in the Laniakea Galaxy on-demand environment

Data-driven vs knowledge-driven inference of health outcomes in the ageing population: A case study

Preventive, Predictive, Personalised and Participative (P4) medicine has the potential to not only vastly improve people's quality of life, but also to significantly reduce healthcare costs and improve its efficiency. Our research focuses on age-related diseases and explores the opportunities offered by a data-driven approach to predict wellness states of ageing individuals, in contrast to the commonly adopted knowledge-driven approach that relies on easy-to-interpret metrics manually introduced by clinical experts. This is done by means of machine learning models applied on the My Smart Age with HIV (MySAwH) dataset, which is collected through a relatively new approach especially for older HIV patient cohorts. This includes Patient Related Outcomes values from mobile smartphone apps and activity traces from commercial-grade activity loggers. Our results show better predictive performance for the data-driven approach. We also show that a post hoc interpretation method applied to the predictive models can provide intelligible explanations that enable new forms of personalised and preventive medicine

Automated Knowledge Graph Completion for Natural Language Understanding: Known Paths and Future Directions

Knowledge Graphs (KGs) are large collections of structured data that can model real world knowledge and are important assets for the companies that employ them. KGs are usually constructed iteratively and often show a sparse structure. Also, as knowledge evolves, KGs must be updated and completed. Many automatic methods for KG Completion (KGC) have been proposed in the literature to reduce the costs associated with manual maintenance. Motivated by an industrial case study aiming to enrich a KG specifically designed for Natural Language Understanding tasks, this paper presents an overview of classical and modern deep learning completion methods. In particular, we delve into Large Language Models (LLMs), which are the most promising deep learning architectures. We show that their applications to KGC are affected by several shortcomings, namely they neglect the structure of KG and treat KGC as a classification problem. Such limitations, together with the brittleness of the LLMs themselves, stress the need to create KGC solutions at the interface between symbolic and neural approaches and lead to the way ahead for future research in intelligible corpus-based KGC

Laniakea : an open solution to provide Galaxy "on-demand" instances over heterogeneous cloud infrastructures

Background: While the popular workflow manager Galaxy is currently made available through several publicly accessible servers, there are scenarios where users can be better served by full administrative control over a private Galaxy instance, including, but not limited to, concerns about data privacy, customisation needs, prioritisation of particular job types, tools development, and training activities. In such cases, a cloud-based Galaxy virtual instance represents an alternative that equips the user with complete control over the Galaxy instance itself without the burden of the hardware and software infrastructure involved in running and maintaining a Galaxy server. Results: We present Laniakea, a complete software solution to set up a \u201cGalaxy on-demand\u201d platform as a service. Building on the INDIGO-DataCloud software stack, Laniakea can be deployed over common cloud architectures usually supported both by public and private e-infrastructures. The user interacts with a Laniakea-based service through a simple front-end that allows a general setup of a Galaxy instance, and then Laniakea takes care of the automatic deployment of the virtual hardware and the software components. At the end of the process, the user gains access with full administrative privileges to a private, production-grade, fully customisable, Galaxy virtual instance and to the underlying virtual machine (VM). Laniakea features deployment of single-server or cluster-backed Galaxy instances, sharing of reference data across multiple instances, data volume encryption, and support for VM image-based, Docker-based, and Ansible recipe-based Galaxy deployments. A Laniakea-based Galaxy on-demand service, named Laniakea@ReCaS, is currently hosted at the ELIXIR-IT ReCaS cloud facility. Conclusions: Laniakea offers to scientific e-infrastructures a complete and easy-to-use software solution to provide a Galaxy on-demand service to their users. Laniakea-based cloud services will help in making Galaxy more accessible to a broader user base by removing most of the burdens involved in deploying and running a Galaxy service. In turn, this will facilitate the adoption of Galaxy in scenarios where classic public instances do not represent an optimal solution. Finally, the implementation of Laniakea can be easily adapted and expanded to support different services and platforms beyond Galaxy

Laniakea: a Galaxy-on-demand Provider Platform Through Cloud Technologies

Galaxy is rapidly becoming the de facto standard workflow manager for bioinformatics. Although several Galaxy public services are currently available, the usage of a private Galaxy instance is still mandatory or preferable for several use cases, including heavy workloads, data privacy concerns or particular customization needs. In this context, cloud computing technologies and infrastructures can provide a powerful and scalable solution to avoid the onerous deployment and maintenance of a local hardware and software infrastructure. Laniakea is a software framework that facilitates the provisioning of on-demand Galaxy instances as a cloud service over e-infrastructures, by leveraging on the open source software catalogue developed by the INDIGO-DataCloud H2020 project, which aimed to make cloud e-infrastructures more accessible by scientific communities. End-users interact with Laniakea through a web front-end that allows a general setup of a Galaxy instance. The deployment of the virtual hardware and of the Galaxy software ecosystem is subsequently performed by the INDIGO Platform as a Service layer. At the end of the process, the user gains access to a private, production-grade, fully customizable, Galaxy virtual instance. Laniakea features the deployment of a stand-alone or cluster backed Galaxy instances, shared reference data volumes, encrypted data volumes and rapid development of novel Galaxy flavours for specific tasks. We present here the latest development iteration of Laniakea, introducing a novel and strongly configurable web interface that facilitates a more straightforward customisation of the user experience through human readable YAML syntax and a reworked encryption procedure that exploits Hashicorp Vault as encryption keys management system

Laniakea@ReCaS: an ELIXIR-ITALY Galaxyon-demand cloud service

Although several Galaxy public services are available, a private Galaxy instance is still mandatory or preferable for several use cases including heavy workloads, data privacy concerns or particular customization needs. Cloud computing technologies provide a viable way to deploy Galaxy private instances, freeing users from the onerous deployment and maintenance of local IT infrastructures. In the last few years, ELIXIR-IT led the development of Laniakea, a software framework that facilitates the provisioning of on-demand Galaxy instances as a cloud service over e-infrastructures. The user interacts with a Laniakea service through a web front-end that allows to configure and launch a production-grade Galaxy instance in a straightforward way. Through the interface, the user can deploy Galaxy instances over single VMs or virtual clusters, link them to shared reference data volumes and plain or encrypted volumes for storing data. A selection of \u201cflavours\u201d, that is Galaxy instances pre-configured with sets of tools for specific tasks, is also available. When the users is satisfied, Laniakea takes oved and deploys the desired Galaxy instance over the cloud, providing a public IP and full administrative privileges over the new instance. In Dec-2018, we launched the beta-test phase of the first Laniakea-based Galaxy on-demand ELIXIR-IT service: Laniakea@ReCaS. After six months of helpful testing, we are now ready to announce the production phase of this service. Access to the service will be provided on a per-project basis through an open-ended call defining terms and conditions, project proposals will be evaluated by a scientific and technical board. Accepted proposals will be granted a package of computational resources for running on-demand Galaxy instances for a duration compatible with the project requirements

VINYL: Variant prIoritizatioN bY survivaL analysis

Motivation: Clinical applications of genome re\uadsequencing technologies typically generate large amounts of data that need to be carefully annotated and interpreted to identify genetic variants associated with pathological conditions. In this context, accurate and reproducible methods for the functional annotation and prioritization of genetic variants are of fundamental importance, especially when large volumes of data \uad like those produced by modern sequencing technologies \uad are involved. Results: In this paper, we present VINYL, a highly accurate and fully automated system for the functional annotation and prioritization of genetic variants in large scale clinical studies. Extensive analyses of both real and simulated datasets suggest that VINYL show higher accuracy and sensitivity when compared to equivalent state of the art methods, allowing the rapid and systematic identification of potentially pathogenic variants in different experimental settings